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This paper provides an overview of the main results achieved within the Horizon 2020 Shift2Rail project named RAILS (Roadmaps for Artificial Intelligence Integration in the Rail Sector). The RAILS roadmapping process provided state-of-the-art, taxonomy, future research directions, and recommendations in three macro areas: Railway Safety and Automation, Predictive Maintenance and Defect Detection, and Traffic Planning and Management. RAILS findings shed light on the potential of intelligent technologies and provided essential guidelines for integrating machine learning into next-generation smart railways.

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Recommendations and Roadmaps Towards Intelligent Railways

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Lorenzo De Donato,
  • Ruifan Tang,
  • Nikola Bešinović,
  • Francesco Flammini,
  • Rob M. P. Goverde,
  • Zhiyuan Lin,
  • Ronghui Liu,
  • Stefano Marrone,
  • Elena Napoletano,
  • Roberto Nardone,
  • Stefania Santini,
  • Valeria Vittorini

摘要

This paper provides an overview of the main results achieved within the Horizon 2020 Shift2Rail project named RAILS (Roadmaps for Artificial Intelligence Integration in the Rail Sector). The RAILS roadmapping process provided state-of-the-art, taxonomy, future research directions, and recommendations in three macro areas: Railway Safety and Automation, Predictive Maintenance and Defect Detection, and Traffic Planning and Management. RAILS findings shed light on the potential of intelligent technologies and provided essential guidelines for integrating machine learning into next-generation smart railways.